Intelligent inspection is an inevitable trend in the field of container security inspection. Automatic classification and recognition of cargoes is an essential component of the intelligent inspection. At present, material recognition technologies based on energy spectrum analysis include X-ray dual energy material recognition technology and neutron-X-ray technology. The dual energy technology is only capable of recognizing organic matters, inorganic matters, mixtures, and heavy metals, which covers a narrow range of categories. The neutron-X-ray technology is capable of recognizing a wide range of categories. However, the neutron generator is very expensive, and the neutron rays are subject to difficult protection and have poor penetration for organic matters. Due to these defects, the neutron-X-ray technology is difficult to be applied in the security inspection of cargoes in containers.
Researches on the automatic classification and recognition technology of cargoes based on analysis of scanned images are progressed with slow paces, and algorithms and functions fail to accommodate user's actual needs. This problem is caused due to two reasons. On one hand, the cargoes are diversified and complicated, and it is hard to find effective features and regular patterns for effective classification. On the other hand, the scanning devices are located in distributed positions, and customs offices keep the scanned images secret such that it is hard to acquire sufficient image data for training a classifier. In addition, image classification and recognition based on mass data analysis imposes higher requirements on algorithms and computing hardware, which thereby brings difficulties to the researches.
At present, researches into the classification and recognition of cargoes are necessary and feasible. On one hand, the problems present in the intelligent inspection are well recognized in the industry and the academic field. For example, the European Union has created a XtRAYner project in the FP7 plan, which is a cross-manufacture intelligent inspection platform. This project is mainly directed to collecting and annotating data, and has launched a research into basic algorithms. On the other hand, image understanding and pattern recognition have gained rapid development in recent years. Advanced algorithms suitable for mass data classification and recognition, such as a Conditional Random Field and deep learning theory are getting perfect, such that the automatic classification and recognition of scanned images of containers will become feasible.